SIMBAD 2011: Venice, Italy

Dissimilarity Characterization and Analysis

• Emilie Morvant, Amaury Habrard, Stéphane Ayache:
  On the Usefulness of Similarity Based Projection Spaces for Transfer Learning. 1-16
  
• Aryeh Kontorovich, Danny Hendler, Eitan Menahem:
  Metric Anomaly Detection via Asymmetric Risk Minimization. 17-30
  
• Orit Kliper-Gross, Tal Hassner, Lior Wolf:
  One Shot Similarity Metric Learning for Action Recognition. 31-45
  
• Bernhard Moser, Gernot Stübl, Jean-Luc Bouchot:
  On a Non-monotonicity Effect of Similarity Measures. 46-60
  
• Oscar Sánchez Siordia, Isaac Martín de Diego, Cristina Conde, Enrique Cabello:
  Section-Wise Similarities for Clustering and Outlier Detection of Subjective Sequential Data. 61-76
  

Generative Models of Similarity Data

• Aydin Ulas, Peter J. Schüffler, Manuele Bicego, Umberto Castellani, Vittorio Murino:
  Hybrid Generative-Discriminative Nucleus Classification of Renal Cell Carcinoma. 77-89
  
• Luca Cazzanti, Sergey Feldman, Maya R. Gupta, Michael Gabbay:
  Multi-task Regularization of Generative Similarity Models. 90-103
  
• André Lourenço, Ana L. N. Fred, Mário A. T. Figueiredo:
  A Generative Dyadic Aspect Model for Evidence Accumulation Clustering. 104-116
  

Graph Based and Relational Models

• Andrea Torsello, Luca Rossi:
  Supervised Learning of Graph Structure. 117-132
  
• Lin Han, Edwin R. Hancock, Richard C. Wilson:
  An Information Theoretic Approach to Learning Generative Graph Prototypes. 133-148
  
• Furqan Aziz, Richard C. Wilson, Edwin R. Hancock:
  Graph Characterization via Backtrackless Paths. 149-162
  
• Aureo Serrano, Luisa Micó, José Oncina:
  Impact of the Initialization in Tree-Based Fast Similarity Search Techniques. 163-176
  

Clustering and Dissimilarity Data

• Aykut Erdem, Erkut Erdem:
  Multiple-Instance Learning with Instance Selection via Dominant Sets. 177-191
  
• Konstantin Voevodski, Maria-Florina Balcan, Heiko Röglin, Shang-Hua Teng, Yu Xia:
  Min-sum Clustering of Protein Sequences with Limited Distance Information. 192-206
  
• Ludwig M. Busse, Joachim M. Buhmann:
  Model-Based Clustering of Inhomogeneous Paired Comparison Data. 207-221
  
• David M. J. Tax, Marco Loog, Robert P. W. Duin, Veronika Cheplygina, Wan-Jui Lee:
  Bag Dissimilarities for Multiple Instance Learning. 222-234
  
• Zhihong Zhang, Edwin R. Hancock:
  Mutual Information Criteria for Feature Selection. 235-249
  

Applications

• Mehmet Gönen, Aydin Ulas, Peter J. Schüffler, Umberto Castellani, Vittorio Murino:
  Combining Data Sources Nonlinearly for Cell Nucleus Classification of Renal Cell Carcinoma. 250-260
  
• Emanuele Olivetti, Paolo Avesani:
  Supervised Segmentation of Fiber Tracts. 261-274
  
• Riccardo Satta, Giorgio Fumera, Fabio Roli:
  Exploiting Dissimilarity Representations for Person Re-identification. 275-289
  

Spectral Methods and Embedding

• Helena Aidos, Ana L. N. Fred:
  A Study of Embedding Methods under the Evidence Accumulation Framework. 290-305
  
• Diana Porro-Muñoz, Robert P. W. Duin, Isneri Talavera-Bustamante, Mauricio Orozco-Alzate:
  A Study on the Influence of Shape in Classifying Small Spectral Data Sets. 306-320
  
• Muhammad Haseeb, Edwin R. Hancock:
  Feature Point Matching Using a Hermitian Property Matrix. 321-332
  

Last update Sat May 25 19:05:39 2013 CET by the DBLP Team — Data released under the ODC-BY 1.0 license — See also our legal information page